Various systems and methods for analysing biological cell assay images exist. Some can be made automated or semi-automated to aid scientists in identifying desirable phenotype responses to certain stimuli, such as drug compounds, in high-throughput screening (HTS) applications.
For example, various methods may be used to apply curve fitting to fit measured cell phenotype data to a mathematical model or expression in order to try and obtain parameters indicative of cellular response to various stimuli to aid in drug discovery [1]. Various other methods use comparisons of real and modelled image data in an attempt to quantify various biological phenotypes, such as spatiotemporal evolution of a given biological or physiological system [2, 3].
However whilst such known techniques are useful for limited sets of circumstances, there is not only a danger of inadequate phenomenological fitting expressions being available to accurately model the assays, but these techniques also generally cannot provide certain important biological data because they rely on averaged measured phenotype responses such as whole field-of-view (FOV) images, for example.